Overview

Dataset statistics

Number of variables24
Number of observations1460
Missing cells3438
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory203.2 KiB
Average record size in memory142.5 B

Variable types

Numeric16
Categorical8

Alerts

YearBuilt has a high cardinality: 112 distinct valuesHigh cardinality
YearRemodAdd has a high cardinality: 61 distinct valuesHigh cardinality
1stFlrSF is highly overall correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with BedroomAbvGr and 1 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
GarageArea is highly overall correlated with GarageYrBlt and 2 other fieldsHigh correlation
GarageYrBlt is highly overall correlated with GarageArea and 2 other fieldsHigh correlation
GrLivArea is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 4 other fieldsHigh correlation
BsmtExposure is highly overall correlated with BsmtFinType1High correlation
BsmtFinType1 is highly overall correlated with BsmtExposureHigh correlation
GarageFinish is highly overall correlated with GarageAreaHigh correlation
KitchenQual is highly overall correlated with OverallQualHigh correlation
OverallQual is highly overall correlated with KitchenQualHigh correlation
YearRemodAdd is highly overall correlated with GarageYrBltHigh correlation
2ndFlrSF has 86 (5.9%) missing valuesMissing
BedroomAbvGr has 99 (6.8%) missing valuesMissing
BsmtFinType1 has 114 (7.8%) missing valuesMissing
EnclosedPorch has 1324 (90.7%) missing valuesMissing
GarageFinish has 162 (11.1%) missing valuesMissing
GarageYrBlt has 81 (5.5%) missing valuesMissing
LotFrontage has 259 (17.7%) missing valuesMissing
WoodDeckSF has 1305 (89.4%) missing valuesMissing
2ndFlrSF has 781 (53.5%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
EnclosedPorch has 116 (7.9%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
MasVnrArea has 861 (59.0%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
WoodDeckSF has 78 (5.3%) zerosZeros

Reproduction

Analysis started2024-07-29 07:12:24.176953
Analysis finished2024-07-29 07:13:02.068946
Duration37.89 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:02.146638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2024-07-29T07:13:02.314188image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct401
Distinct (%)29.2%
Missing86
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean348.52402
Minimum0
Maximum2065
Zeros781
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:02.477810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1142
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation438.86559
Coefficient of variation (CV)1.2592119
Kurtosis-0.54642813
Mean348.52402
Median Absolute Deviation (MAD)0
Skewness0.81512331
Sum478872
Variance192603
MonotonicityNot monotonic
2024-07-29T07:13:02.636877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 781
53.5%
728 10
 
0.7%
672 8
 
0.5%
504 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
546 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
840 5
 
0.3%
Other values (391) 530
36.3%
(Missing) 86
 
5.9%
ValueCountFrequency (%)
0 781
53.5%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 1
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.6%
Missing99
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean2.8692138
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:02.798034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8201148
Coefficient of variation (CV)0.28583258
Kurtosis2.3183658
Mean2.8692138
Median Absolute Deviation (MAD)0
Skewness0.22954095
Sum3905
Variance0.67258828
MonotonicityNot monotonic
2024-07-29T07:13:02.916231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 749
51.3%
2 333
22.8%
4 199
 
13.6%
1 46
 
3.2%
5 20
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
(Missing) 99
 
6.8%
ValueCountFrequency (%)
0 6
 
0.4%
1 46
 
3.2%
2 333
22.8%
3 749
51.3%
4 199
 
13.6%
5 20
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 20
 
1.4%
4 199
 
13.6%
3 749
51.3%
2 333
22.8%
1 46
 
3.2%
0 6
 
0.4%

BsmtExposure
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
No
953 
Av
221 
Gd
134 
Mn
114 
None
 
38

Length

Max length4
Median length2
Mean length2.0520548
Min length2

Characters and Unicode

Total characters2996
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 953
65.3%
Av 221
 
15.1%
Gd 134
 
9.2%
Mn 114
 
7.8%
None 38
 
2.6%

Length

2024-07-29T07:13:03.131598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:03.317678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
no 953
65.3%
av 221
 
15.1%
gd 134
 
9.2%
mn 114
 
7.8%
none 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
N 991
33.1%
o 991
33.1%
A 221
 
7.4%
v 221
 
7.4%
n 152
 
5.1%
G 134
 
4.5%
d 134
 
4.5%
M 114
 
3.8%
e 38
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1536
51.3%
Uppercase Letter 1460
48.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 991
64.5%
v 221
 
14.4%
n 152
 
9.9%
d 134
 
8.7%
e 38
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
N 991
67.9%
A 221
 
15.1%
G 134
 
9.2%
M 114
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2996
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 991
33.1%
o 991
33.1%
A 221
 
7.4%
v 221
 
7.4%
n 152
 
5.1%
G 134
 
4.5%
d 134
 
4.5%
M 114
 
3.8%
e 38
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2996
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 991
33.1%
o 991
33.1%
A 221
 
7.4%
v 221
 
7.4%
n 152
 
5.1%
G 134
 
4.5%
d 134
 
4.5%
M 114
 
3.8%
e 38
 
1.3%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:03.451942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2024-07-29T07:13:03.637904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType1
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.5%
Missing114
Missing (%)7.8%
Memory size1.9 KiB
Unf
396 
GLQ
385 
ALQ
202 
BLQ
136 
Rec
126 
Other values (2)
101 

Length

Max length4
Median length3
Mean length3.0230312
Min length3

Characters and Unicode

Total characters4069
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 396
27.1%
GLQ 385
26.4%
ALQ 202
13.8%
BLQ 136
 
9.3%
Rec 126
 
8.6%
LwQ 70
 
4.8%
None 31
 
2.1%
(Missing) 114
 
7.8%

Length

2024-07-29T07:13:03.812668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:03.979151image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 396
29.4%
glq 385
28.6%
alq 202
15.0%
blq 136
 
10.1%
rec 126
 
9.4%
lwq 70
 
5.2%
none 31
 
2.3%

Most occurring characters

ValueCountFrequency (%)
L 793
19.5%
Q 793
19.5%
n 427
10.5%
U 396
9.7%
f 396
9.7%
G 385
9.5%
A 202
 
5.0%
e 157
 
3.9%
B 136
 
3.3%
R 126
 
3.1%
Other values (4) 258
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2862
70.3%
Lowercase Letter 1207
29.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 793
27.7%
Q 793
27.7%
U 396
13.8%
G 385
13.5%
A 202
 
7.1%
B 136
 
4.8%
R 126
 
4.4%
N 31
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
n 427
35.4%
f 396
32.8%
e 157
 
13.0%
c 126
 
10.4%
w 70
 
5.8%
o 31
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 4069
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 793
19.5%
Q 793
19.5%
n 427
10.5%
U 396
9.7%
f 396
9.7%
G 385
9.5%
A 202
 
5.0%
e 157
 
3.9%
B 136
 
3.3%
R 126
 
3.1%
Other values (4) 258
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 793
19.5%
Q 793
19.5%
n 427
10.5%
U 396
9.7%
f 396
9.7%
G 385
9.5%
A 202
 
5.0%
e 157
 
3.9%
B 136
 
3.3%
R 126
 
3.1%
Other values (4) 258
 
6.3%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:04.113014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2024-07-29T07:13:04.274290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

EnclosedPorch
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)14.0%
Missing1324
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean25.330882
Minimum0
Maximum286
Zeros116
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:04.444784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile218
Maximum286
Range286
Interquartile range (IQR)0

Descriptive statistics

Standard deviation66.684115
Coefficient of variation (CV)2.6325224
Kurtosis5.4086006
Mean25.330882
Median Absolute Deviation (MAD)0
Skewness2.5762649
Sum3445
Variance4446.7712
MonotonicityNot monotonic
2024-07-29T07:13:04.576902image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 116
 
7.9%
112 2
 
0.1%
244 2
 
0.1%
50 1
 
0.1%
286 1
 
0.1%
42 1
 
0.1%
145 1
 
0.1%
190 1
 
0.1%
226 1
 
0.1%
138 1
 
0.1%
Other values (9) 9
 
0.6%
(Missing) 1324
90.7%
ValueCountFrequency (%)
0 116
7.9%
42 1
 
0.1%
50 1
 
0.1%
91 1
 
0.1%
112 2
 
0.1%
136 1
 
0.1%
138 1
 
0.1%
144 1
 
0.1%
145 1
 
0.1%
158 1
 
0.1%
ValueCountFrequency (%)
286 1
0.1%
268 1
0.1%
244 2
0.1%
234 1
0.1%
226 1
0.1%
224 1
0.1%
216 1
0.1%
190 1
0.1%
185 1
0.1%
158 1
0.1%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:04.735605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2024-07-29T07:13:04.914018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

GarageFinish
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.3%
Missing162
Missing (%)11.1%
Memory size1.8 KiB
Unf
546 
RFn
366 
Fin
313 
None
73 

Length

Max length4
Median length3
Mean length3.0562404
Min length3

Characters and Unicode

Total characters3967
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 546
37.4%
RFn 366
25.1%
Fin 313
21.4%
None 73
 
5.0%
(Missing) 162
 
11.1%

Length

2024-07-29T07:13:05.090819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:05.228652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 546
42.1%
rfn 366
28.2%
fin 313
24.1%
none 73
 
5.6%

Most occurring characters

ValueCountFrequency (%)
n 1298
32.7%
F 679
17.1%
U 546
13.8%
f 546
13.8%
R 366
 
9.2%
i 313
 
7.9%
N 73
 
1.8%
o 73
 
1.8%
e 73
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2303
58.1%
Uppercase Letter 1664
41.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1298
56.4%
f 546
23.7%
i 313
 
13.6%
o 73
 
3.2%
e 73
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
F 679
40.8%
U 546
32.8%
R 366
22.0%
N 73
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3967
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1298
32.7%
F 679
17.1%
U 546
13.8%
f 546
13.8%
R 366
 
9.2%
i 313
 
7.9%
N 73
 
1.8%
o 73
 
1.8%
e 73
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1298
32.7%
F 679
17.1%
U 546
13.8%
f 546
13.8%
R 366
 
9.2%
i 313
 
7.9%
N 73
 
1.8%
o 73
 
1.8%
e 73
 
1.8%

GarageYrBlt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:05.360605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.689725
Coefficient of variation (CV)0.012478973
Kurtosis-0.418341
Mean1978.5062
Median Absolute Deviation (MAD)21
Skewness-0.64941462
Sum2728360
Variance609.58251
MonotonicityNot monotonic
2024-07-29T07:13:05.531965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 949
65.0%
(Missing) 81
 
5.5%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%
2005 65
4.5%
2004 53
3.6%
2003 50
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:06.168726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2024-07-29T07:13:06.349109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

KitchenQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 735
50.3%
Gd 586
40.1%
Ex 100
 
6.8%
Fa 39
 
2.7%

Length

2024-07-29T07:13:06.514040image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:06.680972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 735
50.3%
gd 586
40.1%
ex 100
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2195
75.2%
Lowercase Letter 725
 
24.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 735
33.5%
A 735
33.5%
G 586
26.7%
E 100
 
4.6%
F 39
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 586
80.8%
x 100
 
13.8%
a 39
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:06.806508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2024-07-29T07:13:06.966079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

LotFrontage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:07.150496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2024-07-29T07:13:07.322427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:07.517629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2024-07-29T07:13:07.687621image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:07.847577image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2024-07-29T07:13:08.015499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

OverallCond
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
5
821 
6
252 
7
205 
8
 
72
4
 
57
Other values (4)
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row5
2nd row8
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

Length

2024-07-29T07:13:08.186459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:08.338315image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%

OverallQual
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
5
397 
6
374 
7
319 
8
168 
4
116 
Other values (5)
86 

Length

Max length2
Median length1
Mean length1.0123288
Min length1

Characters and Unicode

Total characters1478
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row6
3rd row7
4th row7
5th row8

Common Values

ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%

Length

2024-07-29T07:13:08.481328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-29T07:13:08.645814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
5 397
26.9%
6 374
25.3%
7 319
21.6%
8 168
11.4%
4 116
 
7.8%
9 43
 
2.9%
3 20
 
1.4%
1 20
 
1.4%
0 18
 
1.2%
2 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1478
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 397
26.9%
6 374
25.3%
7 319
21.6%
8 168
11.4%
4 116
 
7.8%
9 43
 
2.9%
3 20
 
1.4%
1 20
 
1.4%
0 18
 
1.2%
2 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1478
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 397
26.9%
6 374
25.3%
7 319
21.6%
8 168
11.4%
4 116
 
7.8%
9 43
 
2.9%
3 20
 
1.4%
1 20
 
1.4%
0 18
 
1.2%
2 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 397
26.9%
6 374
25.3%
7 319
21.6%
8 168
11.4%
4 116
 
7.8%
9 43
 
2.9%
3 20
 
1.4%
1 20
 
1.4%
0 18
 
1.2%
2 3
 
0.2%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:08.800205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2024-07-29T07:13:08.937068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

WoodDeckSF
Real number (ℝ)

MISSING  ZEROS 

Distinct58
Distinct (%)37.4%
Missing1305
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean103.74194
Minimum0
Maximum736
Zeros78
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:09.121571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3182.5
95-th percentile355.3
Maximum736
Range736
Interquartile range (IQR)182.5

Descriptive statistics

Standard deviation135.54315
Coefficient of variation (CV)1.3065416
Kurtosis2.7810699
Mean103.74194
Median Absolute Deviation (MAD)0
Skewness1.5114787
Sum16080
Variance18371.946
MonotonicityNot monotonic
2024-07-29T07:13:09.277714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
5.3%
192 5
 
0.3%
120 4
 
0.3%
100 4
 
0.3%
144 3
 
0.2%
216 2
 
0.1%
196 2
 
0.1%
300 2
 
0.1%
240 2
 
0.1%
160 2
 
0.1%
Other values (48) 51
 
3.5%
(Missing) 1305
89.4%
ValueCountFrequency (%)
0 78
5.3%
24 1
 
0.1%
33 1
 
0.1%
36 1
 
0.1%
44 1
 
0.1%
78 1
 
0.1%
84 1
 
0.1%
100 4
 
0.3%
104 1
 
0.1%
105 1
 
0.1%
ValueCountFrequency (%)
736 1
0.1%
550 1
0.1%
466 1
0.1%
431 1
0.1%
416 1
0.1%
382 1
0.1%
364 1
0.1%
356 1
0.1%
355 1
0.1%
351 1
0.1%

YearBuilt
Categorical

HIGH CARDINALITY 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2006
 
67
2005
 
64
2004
 
54
2007
 
49
2003
 
45
Other values (107)
1181 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.8%

Sample

1st row2003
2nd row1976
3rd row2001
4th row1915
5th row2000

Common Values

ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%

Length

2024-07-29T07:13:09.431832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%

Most occurring characters

ValueCountFrequency (%)
9 1348
23.1%
1 1231
21.1%
0 988
16.9%
2 595
10.2%
6 361
 
6.2%
5 344
 
5.9%
7 338
 
5.8%
8 239
 
4.1%
4 228
 
3.9%
3 168
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1348
23.1%
1 1231
21.1%
0 988
16.9%
2 595
10.2%
6 361
 
6.2%
5 344
 
5.9%
7 338
 
5.8%
8 239
 
4.1%
4 228
 
3.9%
3 168
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 1348
23.1%
1 1231
21.1%
0 988
16.9%
2 595
10.2%
6 361
 
6.2%
5 344
 
5.9%
7 338
 
5.8%
8 239
 
4.1%
4 228
 
3.9%
3 168
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 1348
23.1%
1 1231
21.1%
0 988
16.9%
2 595
10.2%
6 361
 
6.2%
5 344
 
5.9%
7 338
 
5.8%
8 239
 
4.1%
4 228
 
3.9%
3 168
 
2.9%

YearRemodAdd
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
1950
178 
2006
 
97
2007
 
76
2005
 
73
2004
 
62
Other values (56)
974 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2003
2nd row1976
3rd row2002
4th row1970
5th row2000

Common Values

ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%

Length

2024-07-29T07:13:09.581097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%

Most occurring characters

ValueCountFrequency (%)
0 1402
24.0%
9 1259
21.6%
1 987
16.9%
2 663
11.4%
5 423
 
7.2%
7 330
 
5.7%
6 328
 
5.6%
8 216
 
3.7%
4 123
 
2.1%
3 109
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1402
24.0%
9 1259
21.6%
1 987
16.9%
2 663
11.4%
5 423
 
7.2%
7 330
 
5.7%
6 328
 
5.6%
8 216
 
3.7%
4 123
 
2.1%
3 109
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1402
24.0%
9 1259
21.6%
1 987
16.9%
2 663
11.4%
5 423
 
7.2%
7 330
 
5.7%
6 328
 
5.6%
8 216
 
3.7%
4 123
 
2.1%
3 109
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1402
24.0%
9 1259
21.6%
1 987
16.9%
2 663
11.4%
5 423
 
7.2%
7 330
 
5.7%
6 328
 
5.6%
8 216
 
3.7%
4 123
 
2.1%
3 109
 
1.9%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-07-29T07:13:09.719716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2024-07-29T07:13:09.908827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2024-07-29T07:12:58.462560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.264799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.195017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.104730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.251276image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.345785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.414072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.329690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.633545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.681981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.664371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.916679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.908500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.030157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.080341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.490603image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.593841image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.373116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.302027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.233041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.365005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.462656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.543886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.454150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.758970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.790533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.770133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.028135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.025217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.149681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.193291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.625519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.757685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.493969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.413784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.369216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.488848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.579993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.665210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.582702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.887614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.908248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.878566image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.150017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.146545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.268219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.317702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.760958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.918293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.619646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.539862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.513807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.633299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.721482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.777136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.743470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.023780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.043683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.019032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.285767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.286033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.404559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.463133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.869804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.083518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.737753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.654347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.657835image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.767886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.848031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.914968image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.050940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.150448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.166456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.139877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.407723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.431610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.526574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.594663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.004371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.236428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.852079image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.763332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.790297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.892735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.964895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.029589image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.178111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.269703image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.285806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.254337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.529337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.564491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.648413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.717989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.140810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.362770image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:26.978077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.878273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:30.908021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.017059image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.082548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.147268image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.287369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.371015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.401483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.368321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.665097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.693089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.779024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.831477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.248805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.512806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.109948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.002240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.049320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.166996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.218642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.254452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.422232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.495032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.527725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.501744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.808593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.831582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:52.916109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:54.967692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.365150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.654273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.221104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.122422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.180686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.299406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.351121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.364311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.551939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.630544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.652344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.631413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:48.939285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:50.969618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.041301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:55.098601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.476415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.784193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.329372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.231925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.309591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.423154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.476583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.488428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.686948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.758381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.769098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.747862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.050877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.104685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.159317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:55.219480image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.597399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:59.907034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.449084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.343451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.439527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.568181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.596489image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.603494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.820071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:42.887495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:44.883033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:46.862418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.162598image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.227311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.297560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:55.340844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.729605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:13:00.051140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.571325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.459149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.577497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.694624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.720034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.729494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:40.960460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.020761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.007167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.281810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.281285image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.354128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.422840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:55.472286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.852013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:13:00.199135image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.704166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.595852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.729939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.831745image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.858830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.847077image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.105193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.167219image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.144041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.417530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.411548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.493656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.595088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:55.608510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:57.976434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:13:00.339540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.821692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.723721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.858682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:33.958868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:35.996104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:37.964228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.228883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.296776image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.266204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.541638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.527795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.621978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.714934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.072788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.101421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:13:00.483895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:27.948316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.849358image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:31.991005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.088211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.135895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.077952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.364439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.429347image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.398401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.669713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.651466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.756655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.834754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.204473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.215601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:13:00.625332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:28.072808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:29.971405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:32.110580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:34.209250image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:36.262610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:38.204786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:41.486009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:43.548172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:45.534487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:47.788748image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:49.776412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:51.878459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:53.952827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:56.333816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2024-07-29T07:12:58.341733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2024-07-29T07:13:10.095119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
1stFlrSF2ndFlrSFBedroomAbvGrBsmtFinSF1BsmtUnfSFEnclosedPorchGarageAreaGarageYrBltGrLivAreaLotAreaLotFrontageMasVnrAreaOpenPorchSFTotalBsmtSFWoodDeckSFSalePriceBsmtExposureBsmtFinType1GarageFinishKitchenQualOverallCondOverallQualYearRemodAdd
1stFlrSF1.000-0.2720.1450.3230.224-0.1090.4900.2300.4940.4440.4280.3520.2350.8290.1790.5750.1480.0920.1950.2510.0630.2440.149
2ndFlrSF-0.2721.0000.504-0.1860.0600.0970.0990.0720.6470.1280.0560.0620.230-0.279-0.0720.2990.1310.1240.1760.1790.1200.2160.107
BedroomAbvGr0.1450.5041.000-0.0750.1430.1500.106-0.0470.5420.3220.3220.1270.1010.058-0.0000.2300.0950.1070.1330.1360.0740.1310.108
BsmtFinSF10.323-0.186-0.0751.000-0.574-0.0910.2440.0790.0570.1720.1540.2420.0810.4100.2390.3020.2090.2740.1650.2090.0340.2330.116
BsmtUnfSF0.2240.0600.143-0.5741.000-0.1090.1090.1920.2530.0780.1190.0760.1560.329-0.1190.1850.1680.2840.1240.1930.0690.1570.099
EnclosedPorch-0.1090.0970.150-0.091-0.1091.000-0.092-0.307-0.001-0.004-0.008-0.195-0.187-0.239-0.243-0.2150.1950.1210.2050.1440.1340.1540.257
GarageArea0.4900.0990.1060.2440.109-0.0921.0000.5920.4680.3670.3780.3650.3380.4870.1270.6490.1580.1490.6230.3340.1280.2530.161
GarageYrBlt0.2300.072-0.0470.0790.192-0.3070.5921.0000.2810.0420.1160.3060.3940.3380.3080.5940.1490.2900.4010.3930.2360.2470.511
GrLivArea0.4940.6470.5420.0570.253-0.0010.4680.2811.0000.4490.3760.3230.3980.3710.0530.7310.1010.0980.2410.2660.0930.2790.118
LotArea0.4440.1280.3220.1720.078-0.0040.3670.0420.4491.0000.6500.1780.1770.3660.0240.4560.1220.0000.0430.0000.0000.0200.099
LotFrontage0.4280.0560.3220.1540.119-0.0080.3780.1160.3760.6501.0000.2590.1780.3860.1020.4090.1090.0480.1300.1070.0340.1380.082
MasVnrArea0.3520.0620.1270.2420.076-0.1950.3650.3060.3230.1780.2591.0000.2090.3600.1990.4210.0800.0890.1600.1890.0390.1910.000
OpenPorchSF0.2350.2300.1010.0810.156-0.1870.3380.3940.3980.1770.1780.2091.0000.2700.0470.4780.0330.0700.1500.1590.0510.1350.092
TotalBsmtSF0.829-0.2790.0580.4100.329-0.2390.4870.3380.3710.3660.3860.3600.2701.0000.2330.6030.3080.2380.2320.2960.1060.2960.174
WoodDeckSF0.179-0.072-0.0000.239-0.119-0.2430.1270.3080.0530.0240.1020.1990.0470.2331.0000.2520.0660.0750.0000.1830.0960.1620.130
SalePrice0.5750.2990.2300.3020.185-0.2150.6490.5940.7310.4560.4090.4210.4780.6030.2521.0000.2130.2090.3820.4620.1600.3860.210
BsmtExposure0.1480.1310.0950.2090.1680.1950.1580.1490.1010.1220.1090.0800.0330.3080.0660.2131.0000.5200.1550.1510.1030.2110.171
BsmtFinType10.0920.1240.1070.2740.2840.1210.1490.2900.0980.0000.0480.0890.0700.2380.0750.2090.5201.0000.2280.2890.1690.2390.274
GarageFinish0.1950.1760.1330.1650.1240.2050.6230.4010.2410.0430.1300.1600.1500.2320.0000.3820.1550.2281.0000.3170.2270.3670.286
KitchenQual0.2510.1790.1360.2090.1930.1440.3340.3930.2660.0000.1070.1890.1590.2960.1830.4620.1510.2890.3171.0000.2470.5400.433
OverallCond0.0630.1200.0740.0340.0690.1340.1280.2360.0930.0000.0340.0390.0510.1060.0960.1600.1030.1690.2270.2471.0000.3280.162
OverallQual0.2440.2160.1310.2330.1570.1540.2530.2470.2790.0200.1380.1910.1350.2960.1620.3860.2110.2390.3670.5400.3281.0000.213
YearRemodAdd0.1490.1070.1080.1160.0990.2570.1610.5110.1180.0990.0820.0000.0920.1740.1300.2100.1710.2740.2860.4330.1620.2131.000

Missing values

2024-07-29T07:13:00.895169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-29T07:13:01.498143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-29T07:13:01.922658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

1stFlrSF2ndFlrSFBedroomAbvGrBsmtExposureBsmtFinSF1BsmtFinType1BsmtUnfSFEnclosedPorchGarageAreaGarageFinishGarageYrBltGrLivAreaKitchenQualLotAreaLotFrontageMasVnrAreaOpenPorchSFOverallCondOverallQualTotalBsmtSFWoodDeckSFYearBuiltYearRemodAddSalePrice
0856854.03.0No706GLQ1500.0548RFn2003.01710Gd845065.0196.061578560.020032003208500
112620.03.0Gd978ALQ284NaN460RFn1976.01262TA960080.00.00861262NaN19761976181500
2920866.03.0Mn486GLQ4340.0608RFn2001.01786Gd1125068.0162.04257920NaN20012002223500
3961NaNNaNNo216ALQ540NaN642Unf1998.01717Gd955060.00.03557756NaN19151970140000
41145NaN4.0Av655GLQ4900.0836RFn2000.02198Gd1426084.0350.084581145NaN20002000250000
5796566.01.0No732GLQ64NaN480Unf1993.01362TA1411585.00.03055796NaN19931995143000
616940.03.0Av1369GLQ317NaN636RFn2004.01694Gd1008475.0186.057581686NaN20042005307000
71107983.03.0Mn859ALQ216NaN484NaN1973.02090TA10382NaN240.0204671107NaN19731973200000
81022752.02.0No0Unf952NaN468Unf1931.01774TA612051.00.0057952NaN19311950129900
910770.02.0No851GLQ140NaN205RFn1939.01077TA742050.00.0465991NaN19391950118000
1stFlrSF2ndFlrSFBedroomAbvGrBsmtExposureBsmtFinSF1BsmtFinType1BsmtUnfSFEnclosedPorchGarageAreaGarageFinishGarageYrBltGrLivAreaKitchenQualLotAreaLotFrontageMasVnrAreaOpenPorchSFOverallCondOverallQualTotalBsmtSFWoodDeckSFYearBuiltYearRemodAddSalePrice
1450896896.04.0No0Unf896NaN0NoneNaN1792TA900060.00.04555896NaN19741974136000
145115780.03.0No0Unf1573NaN840NaN2008.01578Ex926278.0194.036581573NaN20082009287090
145210720.02.0Gd547GLQ0NaN525Fin2005.01072TA367535.080.02855547NaN20052005145000
145311400.03.0No0Unf11400.00NoneNaN1140TA1721790.00.056551140NaN2006200684500
145412210.02.0No410GLQ8110.0400RFn2004.01221Gd750062.00.0113571221NaN20042005185000
1455953694.03.0No0Unf953NaN460RFn1999.01647TA791762.00.040569530.019992000175000
145620730.0NaNNo790ALQ589NaN500Unf1978.02073TA1317585.0119.00661542NaN19781988210000
145711881152.04.0No275GLQ877NaN252RFn1941.02340Gd904266.00.060971152NaN19412006266500
145810780.02.0Mn49NaN0112.0240Unf1950.01078Gd971768.00.00651078NaN19501996142125
145912560.03.0No830BLQ1360.0276Fin1965.01256TA993775.00.068651256736.019651965147500